Object detection apparatus and method

ABSTRACT

A capture region calculation unit calculates a capture point having the local highest reflection intensity in power profile information and calculates a capture region surrounding the capture point. An edge calculation unit calculates the edges of one or more objects from image data. A marker calculation unit calculates a marker from the capture region. A component region calculation unit calculates component regions by extending the marker using the edges. A grouping unit groups component regions belonging to the same object, of the component regions. The object identification unit identifies the types of one or more objects (e.g., large vehicle, small vehicle, bicycle, pedestrian, flight object, bird) on the basis of a target object region resulting from the grouping.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present disclosure relates to an object detection apparatus andmethod. More specifically, the present disclosure relates to an objectdetection apparatus and method that are used with a vehicle, a roadinfrastructure system, or a system for monitoring a particular facilityand that can individually and accurately detect objects around thevehicle or the like.

2. Description of the Related Art

In recent years, radar apparatuses or camera apparatuses for vehicleshave been mounted on vehicles such as passenger cars and have detectedother vehicles, pedestrians, and bicycles around the vehicles, objectsinstalled on roads, or the like. A radar apparatus or camera apparatusfor vehicles detects a target object approaching the vehicle from thefront or side of the vehicle and measures the position, speed, or thelike of the target object relative to the vehicle. The radar apparatusthen determines whether or not the target object may collide with thevehicle, on the basis of the measurement result. If it determines thatthere is such a risk, the radar apparatus gives an alarm to the driveror automatically controls the vehicle so that the collision is avoided.

For example, Japanese Unexamined Patent Application Publication No.2010-151621 discloses a technology that detects objects using both radarand camera apparatuses for vehicles. Specifically, Japanese UnexaminedPatent Application Publication No. 2010-151621 identifies the number oftarget objects and the azimuth range thereof using measurementinformation acquired by the camera apparatus and corrects measurementinformation acquired by the radar apparatus on the basis of the targetobject number and azimuth range.

U.S. Patent Application Publication No. 2013/0300870 discloses atechnology that monitors traffic volume using both camera and radarapparatuses installed around a road. Specifically, U.S. PatentApplication Publication No. 2013/0300870 monitors and manages traffic bydetecting position and speed information of a remote vehicle using theradar apparatus, identifying the position of the vehicle in a cameraimage, and then presenting the situations of remoter and closer vehiclesthan the vehicle in the camera image.

Conventionally, a radar apparatus or camera apparatus is installed tomonitor a particular facility such as an airport, harbor, railroadstation, or building and prevents intrusion of suspicious objects(including a suspicious person) by detecting an object intruding fromabove the ground or midair (space higher than above the ground) andpresenting information to a related security system or display unit.

SUMMARY

However, the conventional technology of Japanese Unexamined PatentApplication Publication No. 2010-151621 has to identify the number oftarget objects and the azimuth range thereof using measurementinformation acquired by the camera apparatus mounted on a vehicle. Thatis, the vehicle-mounted camera apparatus is required to deliver highobject detection performance.

As for the conventional technology of U.S. Patent ApplicationPublication No. 2013/0300870, when the radar apparatus acquires multipledetection results from one vehicle, it would have difficulty inidentifying the position of the vehicle.

That is, with respect to the above conventional technologies, the objectdetection accuracy depends on the performance of the camera apparatus orradar apparatus, whether the camera apparatus or radar apparatus ismounted on a vehicle or used with a road infrastructure system or asystem for monitoring a particular facility. Accordingly, theseconventional technologies have difficulty in effectively combining thesensing function of a radar apparatus and the sensing function of acamera apparatus to improve object detection accuracy.

Thus, a non-limiting exemplary embodiment of the present disclosureprovides an object detection apparatus and method that can effectivelycombine the sensing function of a radar apparatus and the sensingfunction of a camera apparatus to improve object detection accuracy.

In one general aspect, the techniques disclosed here feature: an objectdetection apparatus including an information generation circuitry which,in operation, calculates a reflection intensity with respect to eachcell of cells, the cells being obtained by dividing a distance from aradar apparatus into predetermined intervals with respect to eachtransmission direction of a radar signal transmitted by the radarapparatus, the reflection intensity being a representative value ofpower of one or more received signals, the one or more received signalsbeing the radar signal reflected by an object and being received by theradar apparatus, and generates power profile information for each cellof the cells by using the reflection intensities, a capture regioncalculation circuitry which, in operation, identifies a cell having alocal highest reflection intensity in the power profile information,from among the cells as a capture point for capturing the object andidentifies one or more cells surrounding the capture point as a captureregion, an edge extraction circuitry which, in operation, extracts edgesof the object included in an image captured by a camera apparatus, amarker calculation circuitry which, in operation, converts the captureregion into a partial region of the image based on a coverage of theradar apparatus and a coverage of the camera apparatus and determinesthe partial region which is a region of the image corresponding to thecapture region, as a marker, a component region calculation circuitrywhich, in operation, determines component regions corresponding tocomponents of the object by extending the marker by using the edges asboundaries, a grouping circuitry which, in operation, groups thecomponent regions into a target object region, and an objectidentification circuitry which, in operation, identifies the object fromthe target object region and outputs the identification result.

These general and specific aspects may be implemented using a device, asystem, a method, and a computer program, and any combination ofdevices, systems, methods, and computer programs.

Additional benefits and advantages of the disclosed embodiments will beapparent from the specification and Figures. The benefits and/oradvantages may be individually provided by the various embodiments andfeatures of the specification and drawings disclosure, and need not allbe provided in order to obtain one or more of the same.

According to the present disclosure, it is possible to effectivelycombine the sensing function of a radar apparatus and the sensingfunction of a camera apparatus to improve object detection accuracy.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A shows the configuration of a sensing unit using an objectdetection apparatus according to the present disclosure;

FIG. 1B shows the configuration of a sensing unit using an objectdetection apparatus according to the present disclosure;

FIG. 2A shows the mounting position of an object detection apparatusaccording to the present disclosure;

FIG. 2B shows the mounting position of an object detection apparatusaccording to the present disclosure;

FIG. 3 shows main elements of an object detection apparatus according toa first embodiment of the present disclosure;

FIG. 4 shows an example of power profile information according to thefirst embodiment of the present disclosure;

FIG. 5 shows an example of calculated capture regions according to thefirst embodiment of the present disclosure;

FIG. 6 shows an example of the coordinate system of a three-dimensionalradar measurement space;

FIG. 7 shows the relationship among the distance, highest possibleheight, and ground distance;

FIG. 8 shows the transformation of coordinates in a three-dimensionalcamera space into coordinates on a camera image plane;

FIG. 9 shows an example of the camera image plane;

FIG. 10 is a diagram showing an example of calculated markerscorresponding to the capture regions shown in FIG. 5;

FIG. 11 is a diagram showing an example in which a component regioncalculation unit divides a marker;

FIG. 12 is a diagram showing an example of the result of regionextension performed by the component region calculation unit;

FIG. 13 shows an example of regions on the radar measurement planeobtained by coordinate-converting component regions.

FIG. 14 is a diagram showing an example of the result of groupingperformed by a grouping unit;

FIG. 15 shows main elements of an object detection apparatus accordingto a second embodiment of the present disclosure.

FIG. 16 shows main elements of an object detection apparatus accordingto a third embodiment of the present disclosure; and

FIG. 17 shows main elements of an object detection apparatus accordingto a fourth embodiment of the present disclosure.

DETAILED DESCRIPTION Underlying Knowledge Forming Basis of the PresentDisclosure

First, underlying knowledge forming the basis of the present disclosurewill be described. The present disclosure relates to radar and cameraapparatuses for vehicles, radar and camera apparatuses for roadinfrastructure systems, and an object detection apparatus for systemsfor monitoring a particular facility.

Currently, radar and camera apparatuses for vehicles are being mountedon many vehicles, and radar and camera apparatuses for roadinfrastructure systems are being introduced to road infrastructuresystems. While a radar apparatus or camera apparatus has been singlyused in systems for monitoring a particular facility, many suchmonitoring systems are currently using both radar and cameraapparatuses.

Radar and camera apparatuses for road infrastructure systems areinstalled in the vicinity of a road, such as an intersection, andmonitor and manage the traffic by detecting vehicles, pedestrians,bicycles, and the like on the road and its vicinity.

Specifically, radar and camera apparatuses for road infrastructuresystems monitor the traffic by detecting traffic volume, as well asspeeding, red light running, and the like and manage the traffic bycontrolling traffic signals on the basis of the detected traffic volume.Further, radar and camera apparatuses for road infrastructure systemsdetect an object in the blind spot of a vehicle and notify the driver ofthe vehicle of information about the detected object. Accordingly, radarand camera apparatuses for road infrastructure systems help make thetraffic efficient and prevent traffic accidents.

Radar and camera apparatuses for vehicles, as well as radar and cameraapparatuses for road infrastructure systems have to accurately detecttarget objects having different features, including vehicles,pedestrians, bicycles, and motorcycles. Radar and camera apparatuses formonitoring systems have to accurately detect various types of vehiclesand pedestrians when monitoring an aboveground area and various types offlight vehicles and birds when monitoring a midair space.

If the above-mentioned radar and camera apparatuses accurately detecttarget objects in midair or above the ground, they can accurately graspthe states of such objects or traffic volume and thus accurately predictthe possibility of intrusion or collision. In contrast, if the radar andcamera apparatuses do not accurately detect target objects in midair orabove the ground, that is, if the radar and camera apparatuses omit orerroneously detect some target objects, they would have difficulty inaccurately grasping the states of such objects or traffic volume andthus predicting the possibility of intrusion or collision.

In a typical measurement, a radar apparatus acquires multiple strongreflection points (hereafter referred to as capture points) from asingle target object. Accordingly, detecting the target object from themeasurement result requires grouping the capture points of the sametarget object.

Japanese Unexamined Patent Application Publication No. 2010-151621identifies the number of target objects and the azimuth range thereof onthe basis of measurement information acquired by the camera apparatusmounted on a vehicle and re-groups or ungroups grouped capture points onthe basis of the number and azimuth range of the target objects. Thus,Japanese Unexamined Patent Application Publication No. 2010-151621avoids erroneous detection or omission of some target objects.

However, with respect to Japanese Unexamined Patent ApplicationPublication No. 2010-151621, the object detection accuracy varies withthe accuracy of the number and azimuth range of target objects, that is,the accuracy of the sensing function of the camera apparatus.

As for U.S. Patent Application Publication No. 2013/0300870, when itacquires multiple capture points from a vehicle serving as a targetobject, it has difficulty in detecting the vehicle, thereby making thistechnology difficult to use.

In view of the foregoing, the present inventors noted that it waspossible to effectively combine measurement information acquired by acamera apparatus and measurement information acquired by a radarapparatus by considering the difference between these types ofinformation, and then accomplished the present disclosure.

Radar and camera apparatuses for vehicles according to the presentdisclosure accurately detect vehicles, bicycles, and pedestrians arounda vehicle having the apparatuses mounted thereon, predict a risk thatanother vehicle or the like may collide with the vehicle, and give analarm or control the vehicle to avoid the risk. Accordingly, the radarand camera apparatuses help prevent traffic accidents.

Radar and camera apparatuses for systems for monitoring a particularfacility, such as an airport, harbor, railroad station, or building,according to the present disclosure accurately detect flight objects andbirds in midair or various types of vehicles and intruders above theground and present information to an external security system.Accordingly, the radar and camera apparatuses help prevent intrusion ofa suspicious person and ensure the safety of the facility.

Radar and camera apparatuses for road infrastructure systems accordingto the present disclosure accurately detect vehicles, bicycles, andpedestrians on a road and its vicinity, including an intersection,predict the possibility of collision, avoid collision, and grasp andmanage traffic volume. Accordingly, the radar and camera apparatuseshelp prevent traffic accidents and make traffic management efficient.

Use Image of Present Disclosure

Hereafter, the connecting method and mounting position of an objectdetection apparatus according to the present disclosure will bedescribed with reference to the drawings.

FIGS. 1A and 1B are conceptual diagrams showing the configuration of asensing unit using an object detection apparatus according to thepresent disclosure. In FIGS. 1A and 1B, R and C represent a radarapparatus and a camera apparatus, respectively, and W represents anobject detection apparatus according to the present disclosure. FIG. 1Ashows a case in which the radar apparatus R and camera apparatus C aremounted in the same casing and connected to the object detectionapparatus W; FIG. 1B shows a case in which the radar apparatus R andcamera apparatus C are mounted in different casings and connected to theobject detection apparatus W. Note that the object detection apparatus Win FIGS. 1A and 1B is further connected to an external security systemor display unit.

The present disclosure does not impose any restriction on the mountingmethod and locations of the radar apparatus R and camera apparatus C orthe relative positions thereof. Nor does it impose any restriction onthe positional relationship between the detection regions of the radarapparatus R and camera apparatus C. However, the present disclosure isapplied to the overlapping region between the detection regions of theradar apparatus R and camera apparatus C and therefore it is preferredto mount the radar apparatus R and camera apparatus C in such a mannerthat the overlapping region is increased.

The present disclosure provides an object detection apparatus thatprocesses measurement information acquired by the radar apparatus R andmeasurement information acquired by the camera apparatus C whilecombining these types of information. The object detection apparatus Waccording to the present disclosure does not impose any restriction onthe configuration of the radar apparatus R nor the configuration of thecamera apparatus C. Both the radar apparatus R and camera apparatus Cmay be existing commercially available products or products manufacturedusing a known technology.

While the object detection apparatus W is mounted independently of theradar apparatus R and camera apparatus C in the conceptual diagramsshown in FIGS. 1A and 1B, the object detection apparatus W may bemounted in the radar apparatus R or camera apparatus C.

In the present disclosure, the radar apparatus R and camera apparatus C,which are connected to the object detection apparatus W, may transmitmeasurement information to the object detection apparatus W using anytransmission method. The transmission method may be a wiredcommunication method or wireless communication method.

Now, the mounting position of the object detection apparatus W accordingto the present disclosure will be described with reference to FIGS. 2Aand 2B. FIGS. 2A and 2B are conceptual diagrams showing the mountingposition of the object detection apparatus W according to the presentdisclosure. FIG. 2A is a conceptual diagram showing that the objectdetection apparatus W is mounted on a vehicle along with the radarapparatus R and camera apparatus C. FIG. 2B is a conceptual diagramshowing that the object detection apparatus W is being used in a roadinfrastructure system along with the radar apparatus R and cameraapparatus C.

In FIG. 2A, V represents the vehicle; R/C represents a measurementapparatus mounted on the vehicle and including the radar apparatus R andcamera apparatus C; and T1 and T2 represent two different targetobjects. The object detection apparatus W may be integral with themeasurement apparatus R/C or may be mounted in a different position fromthe measurement apparatus R/C, as long as the object detection apparatusW can detect objects in front of and on the sides of the vehicle.

In FIG. 2B, R/C represents a measurement apparatus mounted in a roadinfrastructure and including the radar apparatus R and camera apparatusC; p represents the road surface; L represents a support apparatushaving the measurement apparatus R/C mounted thereon, such as a pole;and T1 and T2 represent two different target objects. FIG. 2B is aconceptual perspective view showing the vicinity of the mountingposition of the measurement apparatus R/C.

The road surface p may be a straight road or may be part of anintersection. The mounting position of the measurement apparatus R/C maybe above or on the side of the road or above or at any corner of anintersection. The present disclosure does not impose any restriction onthe mounting position or method of the measurement apparatus R/C, aslong as the measurement apparatus R/C can detect vehicles, pedestrians,bicycles, and the like around a crosswalk at an intersection.

In FIGS. 2A and 2B, the target object T1 is a larger object than thetarget object T2 and is, for example, an object such as a vehicle. Thetarget object T2 is, for example, a motorcycle, bicycle, pedestrian, orthe like. In the conceptual views shown in FIGS. 2A and 2B, the targetobject T2 is located closer to the radar apparatus than the targetobject T1. The object detection apparatus W according to the presentdisclosure detects the target objects T1 and T2 individually.

Although not shown, the object detection apparatus W according to thepresent disclosure may be mounted in a location in which it can monitora particular facility such as an airport, harbor, railroad station, orbuilding. The coverage of the object detection apparatus W according tothe present disclosure is not limited to aboveground regions, and theobject detection apparatus W may be used to monitor or measure midair.

Now, embodiments of the present disclosure will be described in detailwith reference to the drawings. However, the embodiments described beloware illustrative only, and the present disclosure is not limitedthereto.

First Embodiment

First, an object detection apparatus according to a first embodiment ofthe present disclosure will be described with reference to the drawings.FIG. 3 is a block diagram showing main elements of an object detectionapparatus 30 according to the first embodiment of the presentdisclosure.

The object detection apparatus 30 according to the first embodiment ofthe present disclosure is connected to a radar apparatus R and a cameraapparatus C. The radar apparatus R includes a transmission unit thattransmits a radar signal while changing the direction at intervals of apredetermined angle sequentially, a receiving unit that receives theradar signal reflected from a target object as a reflected signal, and asignal processing unit that converts the reflected signal into abaseband signal to acquire a delay profile (propagation delaycharacteristics) for each of the transmission directions of the radarsignal. The camera apparatus C captures an image of the subject (targetobject) to acquire image data.

The object detection apparatus 30 includes an information generationunit 31, a capture region calculation unit 32, a camera imageacquisition unit 33, an edge calculation unit 34, a marker calculationunit 35, a component region calculation unit 36, a grouping unit 37, andan object identification unit 38. The elements of the object detectionapparatus 30 can be implemented as hardware such as an LSI circuit. Theelements of the object detection apparatus 30 can also be implemented aspart of an electronic control unit (ECU) that controls the vehicle.

The information generation unit 31 measures the representative value ofthe received power of the reflected signal (hereafter referred to as“reflection intensity”) for each of cells using the delay profileoutputted from the signal processing unit of the radar apparatus. Thecells are obtained by dividing the distance from the radar apparatusinto predetermined intervals for each of the transmission directions ofthe radar signal. The information generation unit 31 then generatespower profile information indicating the reflection intensity of eachcell and outputs it to the capture region calculation unit 32. While thereflection intensities typically take continuous values, the informationgeneration unit 31 may perform a quantization process to simplify theprocess. Details of the power profile information generated by theinformation generation unit 31 will be described later.

The capture region calculation unit 32 first calculates a point havingthe local highest reflection intensity from the pieces of power profileinformation. The point having the local highest reflection intensitycalculated by the capture region calculation unit 32 serves as a capturepoint for capturing a target object. Specifically, the capture regioncalculation unit 32 calculates the point having the local highestreflection intensity using a known method while handling the pieces ofpower profile information as an image. The capture region calculationunit 32 then calculates a capture region corresponding to the capturepoint using a known image processing method. The capture region is alocal region surrounding the capture point and is composed of pointshaving reflection intensities higher than or equal to a predeterminedvalue, of the points around the capture point. A method by which thecapture region calculation unit 32 calculates a capture region will bedescribed later.

The camera image acquisition unit 33 receives the image data from thecamera apparatus C, performs preprocessing such as image qualityimprovement on the image data, and outputs the resulting image data tothe edge calculation unit 34.

The edge calculation unit 34 calculates the edge (outline) of the targetobject from the image data outputted by the camera image acquisitionunit 33 using a known edge extraction method.

The marker calculation unit 35 calculates a marker from the captureregion calculated by the capture region calculation unit 32. The markeris a partial region corresponding to the capture region, of the cameraimage. A method by which the marker calculation unit 35 calculates amarker will be described later.

The component region calculation unit 36 calculates component regions byextending the marker calculated by the marker calculation unit 35 usingthe edge of the camera image calculated by the edge calculation unit 34.A method by which the component region calculation unit 36 calculatescomponent regions will be described later.

The grouping unit 37 acquires a target object region by groupingcomponent regions belonging to the same object, of the component regionscalculated by the component region calculation unit 36. A method bywhich the grouping unit 37 groups component regions will be describedlater.

The object identification unit 38 identifies the position, size, andshape of the target object, as well as the type thereof (e.g., largevehicle, small vehicle, bicycle, pedestrian) on the basis of the targetobject region resulting from the grouping performed by the grouping unit37. A method by which the object identification unit 38 identifies thetarget objects will be described later. The object identification unit38 then outputs the identification result to an external security systemor display unit.

Next, power profile information generated by the information generationunit 31 will be described. FIG. 4 is a diagram showing an example ofpower profile information according to the first embodiment of thepresent disclosure. The horizontal axis of FIG. 4 represents the azimuthof the radar apparatus R, and the vertical axis thereof represents thedistance from the radar apparatus R. Hereafter, a plane defined by theazimuth range of the radar apparatus R and the region of the distancefrom the radar apparatus R will be referred to as the “radar measurementplane.”

In the example in FIG. 4, cells are formed by dividing the range of theazimuth represented by the horizontal axis into intervals of 10° anddividing the range of the distance represented by the vertical axis intointervals of 10 m. Note that in the present embodiment, the azimuthinterval and distance interval of each cell are not limited to thosedescribed above. Each interval is preferably smaller, since higherresolution is obtained.

In the power profile information in FIG. 4, the density of each cellrepresents the reflection intensity, and a darker cell indicates ahigher reflection intensity. For simplicity, cells other than particularcells are colored in white.

In the present embodiment, it is assumed that the reflection intensity(representative value) of each cell is the highest value of powerreceived in the region of the cell. However, in the present disclosure,the reflection intensity (representative value) of each cell may beother values, for example, the average value of power received in therange of each cell.

In the following description, each cell of power profile information asshown in FIG. 4 is handled as a single point as necessary.

Next, a method by which the capture region calculation unit 32calculates a capture region will be described with reference to FIGS. 4and 5.

The capture region calculation unit 32 first calculates a capture pointfrom the power profile information shown in FIG. 4. The capture point isa point having the local highest reflection intensity in the powerprofile information. The point having the local highest reflectionintensity may be calculated using a known method. For example, the pointhaving the local highest reflection intensity may be calculated asfollows: a comparison is made among the reflection intensity of aparticular point and those of adjacent points; and if the reflectionintensity of the particular point is higher than those of the adjacentpoints by a predetermined value or more, the particular point isregarded as a point having the local highest reflection intensity.

In the case of the power profile information shown in FIG. 4, thecapture region calculation unit 32 calculates points having the localhighest reflection intensity as capture points a1, a2, and a3.

The capture region calculation unit 32 then calculates a capture regionsurrounding the capture points a1, a2, and a3 using a known imageprocessing technique, such as the region growing image processingtechnique, while handling the power profile information as an image. Fordetails of the region growing image processing technique, see R. C.Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 2001.

FIG. 5 is a diagram showing an example of the calculated capture regionaccording to the first embodiment of the present disclosure. Thehorizontal direction of FIG. 5 corresponds to the azimuth of the radarapparatus R, and the vertical direction thereof corresponds to thedistance from the radar apparatus R. Capture regions A1, A2, and A3shown in FIG. 5 are local regions surrounding the capture points a1, a2,and a3, respectively. The capture regions A1, A2, and A3 are also localregions on the radar measurement plane. Typically, a capture region ismore insusceptible to noise than a capture point.

Next, a method by which the marker calculation unit 35 calculates amarker will be described. The marker calculation unit 35 calculates amarker, which is a partial region on a plane of a camera image, from acapture region, which is a local region on the radar measurement plane.Hereafter, a plane defined by the horizontal direction and verticaldirection of a camera image will be referred to as the “camera imageplane.” Since the coordinates on the camera image plane do not match thecoordinates on the radar measurement plane, the marker calculation unit35 calculates a marker from a capture region by performing coordinatetransformation. Hereafter, a case will be described in which the markercalculation unit 35 calculates a marker from the capture region Acorresponding to the target object T1.

Specifically, the marker calculation unit 35 sequentially performs threetypes of coordinate transformation: the transformation of coordinates onthe radar measurement plane into coordinates in a three-dimensionalradar measurement space; the transformation of the coordinates in thethree-dimensional radar measurement space into coordinates in athree-dimensional camera space; and the transformation of thecoordinates in the three-dimensional camera space into coordinates onthe camera image plane.

The three-dimensional radar measurement space is a space scanned by theradar apparatus R, and the three-dimensional camera space is a space inwhich the camera apparatus C captures images. If the radar apparatus Rand camera apparatus C are mounted in different positions, thethree-dimensional radar measurement space and three-dimensional cameraspace may not match each other.

Here it is assumed that the azimuth range of the capture region A on theradar measurement plane is from θ1 to θ2, and the distance range thereonis from d1 to d2. The azimuth range is determined from the minimumazimuth θ1 and maximum azimuth θ2 of the capture region A, and thedistance range is determined from the minimum distance d1 and maximumdistance d2 of the capture region A.

Transformation of Coordinates on Radar Measurement Plane intoCoordinates in Three-Dimensional Radar Measurement Space

First, there will be described the transformation of coordinates on theradar measurement plane into coordinates in the three-dimensional radarmeasurement space. In this transformation, the position and size in thethree-dimensional radar measurement space corresponding to the captureregion A is calculated from the azimuth range θ1 to θ2 and distancerange d1 to d2 of the capture region A.

FIG. 6 is a diagram showing an example of the coordinate system of thethree-dimensional radar measurement space. An origin O and Xr, Yr, andZr axes shown in FIG. 6 represent the coordinate system of thethree-dimensional radar measurement space. The radar apparatus R ismounted on the Zr axis. A height Hr represents the mounting height ofthe radar apparatus R. A distance d from the radar apparatus R shown inFIG. 6 corresponds to a distance d represented by the vertical axis onthe radar measurement plane. A ground distance L is a distance to thetarget object T1 on an Xr-Yr plane (ground or road surface). A height hrepresents the height of the target object T1. The position and shape ofthe target object T1 are schematically shown.

Assuming that a Yr-Zr plane when Xr=0 is a direction of an azimuth θ of0°, the radar apparatus R scans the three-dimensional radar measurementspace shown in FIG. 6 using the Zr axis as a rotation axis. The azimuthθ represented by the horizontal axis of the radar measurement planecorresponds to the projection position of the scan surface of the radarapparatus R on the Xr-Yr plane of the three-dimensional radarmeasurement space. For example, the angle formed by the projectionposition of the scan surface and the Yr axis corresponds to the azimuthθ. FIG. 6 shows a case in which the target object T1 lies in a positioncorresponding to the azimuth θ of 0°.

While the radar apparatus R typically measures a reflection intensitycorresponding to the azimuth θ and distance d, it does not accuratelydetect the direction of the Zr axis in FIG. 6 and, more specifically, anelevation angle φ in FIG. 6. That is, the radar apparatus R cannotdetect the height of the target object T1 from the reflection intensityand thus has difficulty in detecting the ground distance L to the targetobject T1.

For this reason, the marker calculation unit 35 of the presentembodiment presets the highest possible height hp of the target objectT1. The highest possible height hp is the highest value that can betaken as the height of the target object T1. For example, if the targetobject T1 is a pedestrian, the highest possible height hp is set to 2 m.Note that the target object T1 has yet to be identified at this point intime. The highest possible height hp is set on the basis of the size,reflection intensity, or the like of the capture region corresponding tothe target object T1.

The marker calculation unit 35 calculates the range of the grounddistance L to the target object T1 in the three-dimensional radarmeasurement space from the distance d on the radar measurement planeusing the highest possible height hp.

FIG. 7 is a diagram showing the relationship among the distance d,highest possible height hp, and ground distance L. FIG. 7 shows a casein which a signal is reflected by a portion near the ground (Zr=0 inFIG. 7), of the target object T1 and a case in which a signal isreflected by a portion near the highest possible height hp, of thetarget object T1.

As shown in FIG. 7, the ground distance L corresponding to the distanced of a single reflection intensity falls within a range between a grounddistance L1 when a signal is reflected near the ground and a grounddistance L2 when a signal is reflected near the highest possible heighthp.

With respect to the distance range d1 to d2 of the capture region A, themarker calculation unit 35 calculates the ground distance L1 (L11)corresponding to the distance d1 and the ground distance L2 (L12)corresponding to the distance d1, as well as calculates the grounddistance L1 (L21) corresponding to the distance d2 and the grounddistance L2 (L22) corresponding to the distance d2. The markercalculation unit 35 then determines the minimum value Lmin and maximumvalue Lmax of L11, L12, L21, and L22. The marker calculation unit 35then calculates a ground distance range Lmin to Lmax in the Yr axisdirection from the distance range d1 to d2 of the capture region A.

As described above, the azimuth θ represented by the horizontal axis ofthe radar measurement plane corresponds to the projection position ofthe scan surface of the radar apparatus R on the Xr-Yr plane.Accordingly, the marker calculation unit 35 calculates the azimuth rangeθ1 to θ2 of the target object T1 on the Xr-Yr plane from the azimuthrange θ1 to θ2.

Transformation of Coordinates in Three-Dimensional Radar MeasurementSpace into Coordinates in Three-Dimensional Camera Space

Next, there will be described the transformation of coordinates in thethree-dimensional radar measurement space into coordinates in thethree-dimensional camera space. Since the mounting positions of theradar apparatus R and camera apparatus C are known, coordinates in thethree-dimensional radar measurement space are transformed intocoordinates in the three-dimensional camera space using a typicalcoordinate transformation method.

By performing this transformation, a marker, which is a partial regionon the camera image plane, can be calculated from a capture region,which is a local region on the radar measurement plane, even when theradar apparatus R and camera apparatus C are mounted in differentpositions.

For simplicity, it is assumed that the three-dimensional camera space isthe same as the three-dimensional radar measurement space having theXr-Yr-Zr coordinate system. That is, in the following description, theazimuth range θ1 to θ2 in the three-dimensional radar measurement spaceand the ground distance range Lmin to Lmax in the Yr axis direction areapplied to the three-dimensional camera space as they are.

Transformation of Coordinates in Three-Dimensional Camera Space intoCoordinates on Camera Image Plane

Next, there will be described the transformation of coordinates in thethree-dimensional camera space into coordinates on the camera imageplane. In this transformation, the azimuth range θ1 to θ2 in thethree-dimensional camera space (which is the same as thethree-dimensional radar measurement space) and the ground distance rangeLmin to Lmax in the Yr axis direction are converted into correspondingranges on the camera image plane. The ranges on the camera image planeresulting from this transformation, that is, a partial region on thecamera image plane are a marker corresponding to the capture region A.

First, there will be described a method for converting the grounddistance range Lmin to Lmax in the Yr axis direction in thethree-dimensional camera space into a corresponding range on the cameraimage plane.

FIG. 8 is a diagram showing the transformation of coordinates in thethree-dimensional camera space into coordinates on the camera imageplane. FIG. 9 is a diagram showing an example of the camera image plane.FIG. 9 schematically shows an image captured by the camera apparatus Cin the space shown in FIG. 8. While the camera image plane is shown inFIG. 9 for description purposes, the marker calculation unit 35 uses anactually captured image, that is, an image acquired by the camera imageacquisition unit 33.

An origin O and Xr, Yr, and Zr axes shown in FIG. 8 represent thecoordinate system of the three-dimensional camera space. The cameraapparatus C is mounted on the Zr axis. A height Hc represents themounting height of the camera apparatus C. In the following description,it is assumed that the position of the camera apparatus C and, morespecifically, the center point when the camera apparatus C captures animage is a point C and that the point C is located at the height Hc onthe Zr axis.

An angle ∠PCQ shown in FIG. 8 is the vertical view angle of the cameraapparatus C. A point P and a point Q shown in FIGS. 8 and 9 correspondto the lower limit and upper limit, respectively, of the view anglerange of the camera apparatus C. The point P and point Q are calculatedfrom the view angle range of the camera apparatus C.

A Yr-Zr plane when Xr=0 in FIG. 8 corresponds to a PQ segment in FIG. 9.Xr=0 in FIG. 8 corresponds to the center of the horizontal view anglerange of the camera apparatus C.

As shown in FIG. 9, a vanishing point F shown in FIG. 8 is an infinitepoint on a road surface p in the camera image plane. The vanishing pointF is calculated using a known method.

The ground distance range Lmin to Lmax shown in FIG. 8 is a grounddistance range obtained by converting the coordinates on the radarmeasurement plane into coordinates in the three-dimensional radarmeasurement space. In the following description, it is assumed that theground distance range Lmin to Lmax is the range from a point K to apoint J on the Yr axis.

As shown in FIGS. 8 and 9, a point V and a point U on the camera imageplane correspond to the point J and point K, respectively. A range onthe camera image plane corresponding to the ground distance range Lminto Lmax in the Yr axis direction is calculated by calculating thepositions of the point U and point V on the camera image plane.

First, a method for calculating the position of the point U on thecamera image plane will be described.

A relational expression ∠PCF:∠PCQ=PF:PQ holds true for the vanishingpoint F, point P, and point Q. ∠PCF and ∠PCQ are angles in thethree-dimensional camera space shown in FIG. 8, and PF and PQ arelengths on the camera image plane shown in FIG. 9. More specifically,∠PCQ is the vertical view angle range of the camera apparatus C, and PQis the vertical width of the camera image. Accordingly, both are knownvalues determined by the specification of the camera apparatus C. Thevanishing point F is calculated using a known method and therefore PF isalso known. ∠PCF is calculated from the above relational expression.

Next, as shown in FIG. 8, ∠OKC is calculated from the height Hc, whichis a length of OC, and the ground distance Lmin, which is a length OK,using a trigonometric function or the like. Since a straight lineconnecting the point C and point F is parallel with the Yr axis in FIG.8, the calculated ∠OKC is the same as ∠UCF.

Next, a relational expression ∠UCF:∠PCF=UF:PF holds for the calculated∠PCF and ∠UCF. PF and UF are lengths on the camera image plane shown inFIG. 9. The length UF is calculated from this relational expression.

The position of the point U on the camera image plane shown in FIG. 9 iscalculated from the calculated UF. The point V on the camera image planeshown in FIG. 9 is calculated in a manner similar to that for the pointU.

As described above, the marker calculation unit 35 calculates thepositions of the point U and point V on the camera image plane from theground distance range Lmin to Lmax in the Yr axis direction.

Next, there will be described a method for calculating a range on thecamera image plane corresponding to the azimuth range θ1 to θ2 in thethree-dimensional camera space.

The azimuth in the three-dimensional camera space corresponds to thehorizontal distance from PQ on the camera image plane shown in FIG. 9.The horizontal view angle range of the camera apparatus C is a knownrange determined according to the specification and corresponds to thehorizontal left and right edges of the camera image plane. The markercalculation unit 35 calculates ranges on the camera image planecorresponding to the azimuth range θ1 to θ2 and the horizontal viewangle range of the camera apparatus C. That is, it calculates thehorizontal distance from PQ.

Vertical lines θ1 and θ2 shown in FIG. 9 correspond to θ1 and θ2,respectively, of the azimuth range.

As described above, the marker calculation unit 35 calculates ranges onthe camera image plane corresponding to the azimuth range θ1 to θ2 andground distance range Lmin to Lmax in the Yr axis direction in thethree-dimensional camera space. The marker calculation unit 35 thenregards a rectangular frame surrounding the calculated ranges as amarker. A marker B in FIG. 9 is a marker corresponding to the captureregion A. The marker B is a rectangle surrounded by horizontal straightlines passing through the calculated points U and V and the lines θ1 andθ2.

FIG. 10 is a diagram showing an example of calculated markerscorresponding to the capture regions shown in FIG. 5. Specifically, FIG.10 shows markers B1, B2, and B3 on the camera image plane correspondingto the capture regions A1, A2, and A3 shown in FIG. 5. In FIG. 10, themarkers B1, B2, and B3 are superimposed on the edge of the camera imagecalculated by the edge calculation unit 34. As shown in FIG. 10, themarkers on the camera image plane are calculated from the captureregions on the radar measurement plane, as rectangles.

The method for calculating a marker using coordinate transformationdescribed above is illustrative only, and the present disclosure is notlimited thereto. The marker calculation unit 35 may convert captureregions and calculate markers on the basis of the azimuth and distanceranges in which the radar apparatus R can make measurements in realspace and the range in which the camera apparatus C can capture imagesin real space. The azimuth and distance ranges in which the radarapparatus R can make measurements in real space are previouslydetermined by the mounting position and specification of the radarapparatus R. The range in which the camera apparatus C can captureimages in real space is previously determined by the mounting positionand specification of the camera apparatus C.

While the markers described above are rectangles, the markers may be inshapes other than rectangles in the present disclosure.

Next, a method by which the component region calculation unit 36calculates component regions will be described.

First, the component region calculation unit 36 divides a markersuperimposed on an edge. In the case of FIG. 10, the marker B2 issuperimposed on the edge and therefore the component region calculationunit 36 divides the marker B2.

FIG. 11 is a diagram showing an example in which the component regioncalculation unit 36 divides a marker. As shown in FIG. 11, the marker B2superimposed on the edge in FIG. 10 is divided into markers B21 and B22.

The component region calculation unit 36 then calculates componentregions by extending the regions using a known image processingtechnique, such as a watershed algorithm, while using the markers asseeds for range extension and using the edge as a boundary for rangeextension. As used herein, a component region refers to a partial regionon the camera image plane corresponding to one of the components of anobject.

FIG. 12 is a diagram showing an example of the result of the regionextension performed by the component region calculation unit 36. As theresult of the region extension, a component region C1 is calculated fromthe markers B1 and B22; a component region C2 is calculated from themarker B21; and a component region C3 is calculated from the marker B3.

Next, a method by which the grouping unit 37 groups component regionswill be described.

The grouping unit 37 groups component regions belonging to the sameobject, of the component regions calculated by the component regioncalculation unit 36. Whether a component region belongs to the sameobject is determined on the basis of one or both of information obtainedfrom the camera image and information obtained by radar measurement.

The information obtained from the camera image is, for example, thetextures of the component regions in the camera image. The grouping unit37 makes a comparison among the textures of adjacent component regionsand, if the textures are similar, groups the adjacent component regions.Whether the textures are similar may be determined on the basis of apredetermined threshold or the like.

The information obtained by radar measurement is, for example, Dopplerinformation. As used herein, the “Doppler information” refers to speedinformation of points on the radar measurement plane. Accordingly, inorder to determine whether a component region belongs to the same objectusing the Doppler information, it is necessary to coordinate-convert thecomponent region, which a region on the camera image plane, into aregion on the radar measurement plane.

The component region is coordinate-converted into a region on the radarmeasurement plane by only reversely performing the above-mentionedmethod for calculating a marker from a capture region.

FIG. 13 is a diagram showing an example of regions on the radarmeasurement plane obtained by coordinate-converting component regions.The horizontal direction of FIG. 13 corresponds to the azimuth of theradar apparatus R; the vertical direction thereof corresponds to thedistance from the radar apparatus R; and each point (each cell) containsDoppler information. Regions D1, D2, and D3 in FIG. 13 correspond to thecomponent regions C1, C2, and C3, respectively, shown in FIG. 12.

The grouping unit 37 makes a comparison among the pieces of Dopplerinformation contained in the regions D1, D2, and D3 and, if the piecesof Doppler information are similar, groups the adjacent componentregions on the camera image plane. Whether the pieces of Dopplerinformation are similar may be determined on the basis of apredetermined threshold.

FIG. 14 is a diagram showing an example of the result of the groupingperformed by the grouping unit 37. As shown in FIG. 14, the componentregions C1 and C2 in FIG. 12 are grouped into a target object region E1;the component region C3 in FIG. 12 serves as a target object region E2without being grouped.

In the example shown in FIG. 14, the grouping unit 37 acquires the twotarget object regions, E1 and E2, as the result of the grouping.

Next, a method by which the object identification unit 38 identifiestarget objects will be described.

The object identification unit 38 identifies the positions, sizes, andshapes of the target objects, as well as the types thereof on the basisof the target object regions resulting from the grouping performed bythe grouping unit 37. In the first embodiment of the present disclosure,the identification method used by the object identification unit 38 isnot limited to any specific one. For example, the object identificationunit 38 may identify the target objects by previously holding templatemodels indicating the sizes and shapes of target object regionscorresponding to the types of objects and then comparing the targetobject regions resulting from the grouping performed by the groupingunit 37 with the template models. The object identification unit 38 mayalso identify the target objects by comparing the target object regionswith a template model indicating the distribution of reflectionintensities corresponding to the types of objects.

As an example, a case will be described in which the objectidentification unit 38 identifies the target objects by comparing thetarget object regions E1 and E2 shown in FIG. 14 with template models.The object identification unit 38 compares the target object region E1with multiple template models held thereby and determines that thetarget object region E1 matches a vehicle template model. The objectidentification unit 38 also compares the target object region E2 withmultiple template models held thereby and determines that the targetobject region E2 matches a pedestrian template model.

According to the present embodiment, the target object detectionaccuracy can be improved by converting capture regions on the radarmeasurement plane into markers on the camera image plane andsuperimposing the markers on the camera image. That is, the targetobject detection accuracy can be improved by effectively superimposingmeasurement information acquired by the radar on measurement informationacquired by the camera.

Second Embodiment

FIG. 15 is a block diagram showing main elements of an object detectionapparatus 150 according to a second embodiment of the presentdisclosure. In FIG. 15, the same elements as those in FIG. 3 are giventhe same reference signs as those in FIG. 3 and therefore detaileddescription thereof will be omitted. An object detection apparatus 150shown in FIG. 15 has a configuration in which the information generationunit 31 and capture region calculation unit 32 of the object detectionapparatus 30 shown in FIG. 3 are replaced with an information generationunit 151 and a capture region calculation unit 152, respectively.

As with the information generation unit 31 of the first embodiment, theinformation generation unit 151 generates power profile information. Theinformation generation unit 151 further generates Doppler profileinformation indicating the Doppler speeds of cells from a delay profilereceived from a radar apparatus R. In the Doppler profile information,the horizontal axis represents the azimuth, and the vertical axisrepresents the distance.

The capture region calculation unit 152 calculates a capture region onthe basis of the power profile information and Doppler profileinformation.

Specifically, the capture region calculation unit 152 calculates acapture region from the power profile information using the methoddescribed in the first embodiment. The capture region calculation unit152 then makes a comparison among the Doppler speeds of points (cells)included in the capture region and identifies whether the Doppler speedsmatch each other. The capture region calculation unit 152 then removespoints (cells) having inconsistent Doppler profile values from thecapture region.

The capture region calculation unit 152 outputs the resulting captureregion to a marker calculation unit 35. The marker calculation unit 35and subsequent elements perform processes similar to those described inthe first embodiment.

According to the present embodiment, some points (cells) are removedfrom a capture region using the Doppler speed. Thus, it is possible toavoid the reflection intensities of signals reflected from differentobjects being included in the capture region.

While, in the present embodiment, the capture region calculation unit152 calculates a capture region on the basis of power profileinformation and Doppler profile information, it may calculate a captureregion on the basis of Doppler profile information.

Third Embodiment

FIG. 16 is a block diagram showing main elements of an object detectionapparatus 160 according to a third embodiment of the present disclosure.In FIG. 16, the same elements as those in FIG. 3 are given the samereference signs as those in FIG. 3 and therefore detailed descriptionthereof will be omitted. The object detection apparatus 160 shown inFIG. 16 has a configuration in which a model frame identification unit161 is inserted between the grouping unit 37 and object identificationunit 38 of the object detection apparatus 30 shown in FIG. 3.

The model frame identification unit 161 obtains a model frame forcovering a target object region resulting from grouping performed by thegrouping unit 37. The model frame is a frame reflecting the shape of thetarget object and is, for example, a rectangular frame.

The model frame identification unit 161 then covers the target objectregion with the obtained model frame to supplement the target objectregion in which the grouping unit 37 has insufficiently groupedcomponent regions.

According to the present embodiment, a target object region issupplemented using a model frame. Thus, it is possible to make the shapeof the target object region analogous to the shape of a correspondingobject and thus to improve the object identification accuracy of theobject identification unit 38.

Fourth Embodiment

FIG. 17 is a block diagram showing main elements of an object detectionapparatus 170 according to a fourth embodiment of the presentdisclosure. In FIG. 17, the same elements as those in FIG. 3 are giventhe same reference signs as those in FIG. 3 and therefore detaileddescription thereof will be omitted. The object detection apparatus 170shown in FIG. 17 has a configuration in which a region tracking unit 171is inserted between the grouping unit 37 and object identification unit38 of the object detection apparatus 30 shown in FIG. 3.

The region tracking unit 171 tracks the position and shape of a targetobject region resulting from grouping performed by the grouping unit 37during the period between different times.

Specifically, the region tracking unit 171 holds a target object regionat a certain detection timing, t1. The region tracking unit 171 receivesanother target object region from the grouping unit 37 at a subsequentdetection timing, t2, and links the target object region held at thedetection timing t1 and the target object region received at thedetection timing t2. The region tracking unit 171 then tracks changes inthe shapes or positions of the linked target object regions to detectthe movements of the target object regions.

The region tracking unit 171 then outputs information about themovements of the target object regions to an object identification unit38. The object identification unit 38 refers to the information aboutthe movements of the target object regions to identify target objectsfrom the target object regions. After identifying the target objects,the object identification unit 38 may output information about thetarget objects as well as information about the movements of the targetobjects to an external display unit, security system, or the like.

According to the present embodiment, the positions and shapes of targetobject regions are tracked during the period between different detectiontimings, and the movements of the target object regions are detected.Thus, it is possible to improve the object identification accuracy, aswell as to obtain information about the movements of correspondingobjects.

The embodiments described above may be combined as necessary. Forexample, in the object detection apparatus 170 according to the fourthembodiment, the model frame identification unit 161 described in thethird embodiment may be inserted between the grouping unit 37 and regiontracking unit 171. According to this configuration, it is possible tomake the shape of a target object region more analogous to the shape ofa corresponding object, as well as to improve the accuracy with whichthe region tracking unit 171 detects the movement of a target object.

While the example in which the present disclosure is implemented ashardware has been described in the above embodiments, the presentdisclosure may be implemented as software.

The method for forming an integrated circuit is not limited to LSI andmay be to use a dedicated circuit or general-purpose processor. Aftermanufacturing the LSI, a field programmable gate array (FPGA) may beused, or a reconfigurable processor, which can reconfigure theconnection or setting of the circuit cells in the LSI, may be used.

If an integrated circuit technology which replaces LSI appears due tothe progress of the semiconductor technology or due to a derivedtechnology, the function blocks may be integrated using that technology,as a matter of course. Possible such technologies include biotechnologies.

The object detection apparatuses and methods according to the presentdisclosure are suitably used with radar and camera apparatuses forvehicles, radar and camera apparatuses for road infrastructure systems,and radar and camera apparatuses for facility monitoring systems. Whenany of the object detection apparatuses and methods is mounted on avehicle along with radar and camera apparatuses for vehicles, the objectdetection apparatus or method detects pedestrians, bicycles, and othervehicles around the vehicle and give an alarm to the driver of thevehicle or control the drive system. Accordingly, the object detectionapparatus or method helps avoid the risk of collision. When any of theobject detection apparatuses and methods is used with radar and cameraapparatuses for infrastructure systems, the object detection apparatusor method monitors the traffic of a road or an intersection by detectingpedestrians, bicycles, vehicles, and the like, as well as controls theinfrastructure or transmits information to the driver of a vehicle.Accordingly, the object detection apparatus or method helps manage thetraffic volume and avoid traffic accidents. When any of the objectdetection apparatuses and methods is used with radar and cameraapparatuses for systems for monitoring a particular facility, the objectdetection apparatus or method detects flight objects and birds in midairor various types of vehicles and intruders above the ground andtransmits information to a security system. Accordingly, the objectdetection apparatus or method helps prevent intrusion of a suspiciousperson.

What is claimed is:
 1. An object detection apparatus comprising: aninformation generation circuitry which, in operation, calculates areflection intensity with respect to each cell of cells, the cells beingobtained by dividing a distance from a radar apparatus intopredetermined intervals with respect to each transmission direction of aradar signal transmitted by the radar apparatus, the reflectionintensity being a representative value of power of one or more receivedsignals, the one or more received signals being the radar signalreflected by an object and being received by the radar apparatus, andgenerates power profile information for each cell of the cells by usingthe reflection intensities; a capture region calculation circuitrywhich, in operation, identifies a cell having a local highest reflectionintensity in the power profile information, from among the cells as acapture point for capturing the object and identifies one or more cellssurrounding the capture point as a capture region; an edge extractioncircuitry which, in operation, extracts edges of the object included inan image captured by a camera apparatus; a marker calculation circuitrywhich, in operation, converts the capture region into a partial regionof the image based on a coverage of the radar apparatus and a coverageof the camera apparatus and determines the partial region which is aregion of the image corresponding to the capture region, as a marker; acomponent region calculation circuitry which, in operation, determinescomponent regions corresponding to components of the object by extendingthe marker by using the edges as boundaries; a grouping circuitry which,in operation, groups the component regions into a target object region;and an object identification circuitry which, in operation, identifiesthe object from the target object region and outputs the identificationresult.
 2. The object detection apparatus according to claim 1, whereinthe marker calculation circuitry converts the capture region into themarker by using predetermined height information of the object.
 3. Theobject detection apparatus according to claim 1, wherein the componentregion calculation circuitry superimposes the marker on the edges anddivides the marker by an edge of the edges when the marker extends overthe edge.
 4. The object detection apparatus according to claim 1,wherein the information generation circuitry calculates a Doppler speedfor each cell of the cells based on a delay profile obtained from theone or more received signals by the radar apparatus and generatesDoppler profile information indicating the Doppler speed for each cellof the cells, and the capture region calculation circuitry compares eachDoppler speed of the cells included in the capture region and removes acell having an inconsistent Doppler profile value from the captureregion.
 5. The object detection apparatus according to claim 1, furthercomprising a model frame identification circuitry that target objectregion supplements the target object region with a model frame whichcovers the target object region for groping the component regions. 6.The object detection apparatus according to claim 1, further comprisinga region tracking circuitry which, in operation, tracks a change in ashape of the target object region associated with passage of time todetect information about a movement of the target object region.
 7. Aradar apparatus for vehicles, comprising: the object detection apparatusaccording to claim 1; and a radar apparatus connected to the objectdetection apparatus.
 8. A radar apparatus for road infrastructuresystems, comprising: the object detection apparatus according to claim1; and a radar apparatus connected to the object detection apparatus. 9.A radar apparatus for monitoring systems, comprising: the objectdetection apparatus according to claim 1; and a radar apparatusconnected to the object detection apparatus.
 10. An object detectionmethod comprising: calculating a reflection intensity with respect toeach cell of cells, the cells being obtained by dividing a distance froma radar apparatus into predetermined intervals with respect to eachtransmission direction of a radar signal transmitted by the radarapparatus, the reflection intensity being a representative value ofpower of one or more received signals, the one or more received signalsbeing the radar signal reflected by an object and being received by theradar apparatus, and generating power profile information for each cellof the cells by using the reflection intensities; identifying a cellhaving a local highest reflection intensity in the power profileinformation, from among the cells as a capture point for capturing theobject and identifying one or more cells surrounding the capture pointas a capture region; extracting edges of the object included in an imagecaptured by a camera apparatus; converting the capture region into apartial region of the image based on a coverage of the radar apparatusand a coverage of the camera apparatus and determining the partialregion which is a region of the image corresponding to the captureregion, as a marker; determining component regions corresponding tocomponents of the object by extending the marker by using the edges asboundaries; grouping the component regions into a target object region;and identifying the object from the target object region and outputtingthe identification result.